Overview

Dataset statistics

Number of variables24
Number of observations101
Missing cells4
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.1 KiB
Average record size in memory193.3 B

Variable types

Numeric14
Categorical10

Alerts

df_index is highly correlated with MWT1 and 6 other fieldsHigh correlation
AGE is highly correlated with AGEquartilesHigh correlation
MWT1 is highly correlated with df_index and 7 other fieldsHigh correlation
MWT2 is highly correlated with df_index and 5 other fieldsHigh correlation
MWT1Best is highly correlated with df_index and 3 other fieldsHigh correlation
FEV1 is highly correlated with COPDSEVERITY and 3 other fieldsHigh correlation
FEV1PRED is highly correlated with df_index and 8 other fieldsHigh correlation
FVC is highly correlated with PackHistory and 5 other fieldsHigh correlation
FVCPRED is highly correlated with df_index and 5 other fieldsHigh correlation
CAT is highly correlated with MWT2 and 1 other fieldsHigh correlation
HAD is highly correlated with SGRQHigh correlation
SGRQ is highly correlated with df_index and 3 other fieldsHigh correlation
AGEquartiles is highly correlated with AGEHigh correlation
copd is highly correlated with COPDSEVERITY and 4 other fieldsHigh correlation
AtrialFib is highly correlated with df_index and 3 other fieldsHigh correlation
COPDSEVERITY is highly correlated with FEV1 and 4 other fieldsHigh correlation
ID is highly correlated with FEV1PREDHigh correlation
PackHistory is highly correlated with FVCHigh correlation
IHD is highly correlated with MWT1High correlation
MWT1 has 2 (2.0%) missing values Missing
df_index is uniformly distributed Uniform
df_index has unique values Unique
HAD has 4 (4.0%) zeros Zeros

Reproduction

Analysis started2022-09-19 20:24:35.241581
Analysis finished2022-09-19 20:25:20.501429
Duration45.26 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct101
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51
Minimum1
Maximum101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size936.0 B
2022-09-19T22:25:20.660726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q126
median51
Q376
95-th percentile96
Maximum101
Range100
Interquartile range (IQR)50

Descriptive statistics

Standard deviation29.30017065
Coefficient of variation (CV)0.57451315
Kurtosis-1.2
Mean51
Median Absolute Deviation (MAD)25
Skewness0
Sum5151
Variance858.5
MonotonicityStrictly increasing
2022-09-19T22:25:20.878725image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
1.0%
651
 
1.0%
751
 
1.0%
741
 
1.0%
731
 
1.0%
721
 
1.0%
711
 
1.0%
701
 
1.0%
691
 
1.0%
681
 
1.0%
Other values (91)91
90.1%
ValueCountFrequency (%)
11
1.0%
21
1.0%
31
1.0%
41
1.0%
51
1.0%
61
1.0%
71
1.0%
81
1.0%
91
1.0%
101
1.0%
ValueCountFrequency (%)
1011
1.0%
1001
1.0%
991
1.0%
981
1.0%
971
1.0%
961
1.0%
951
1.0%
941
1.0%
931
1.0%
921
1.0%

ID
Real number (ℝ≥0)

HIGH CORRELATION

Distinct97
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.40594059
Minimum1
Maximum169
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size936.0 B
2022-09-19T22:25:21.150940image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q149
median87
Q3143
95-th percentile164
Maximum169
Range168
Interquartile range (IQR)94

Descriptive statistics

Standard deviation51.51624564
Coefficient of variation (CV)0.5635984412
Kurtosis-1.267318994
Mean91.40594059
Median Absolute Deviation (MAD)46
Skewness-0.0517231265
Sum9232
Variance2653.923564
MonotonicityNot monotonic
2022-09-19T22:25:21.767526image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1622
 
2.0%
1582
 
2.0%
1592
 
2.0%
1572
 
2.0%
581
 
1.0%
541
 
1.0%
301
 
1.0%
31
 
1.0%
1401
 
1.0%
1691
 
1.0%
Other values (87)87
86.1%
ValueCountFrequency (%)
11
1.0%
21
1.0%
31
1.0%
61
1.0%
81
1.0%
101
1.0%
111
1.0%
121
1.0%
151
1.0%
161
1.0%
ValueCountFrequency (%)
1691
1.0%
1681
1.0%
1671
1.0%
1661
1.0%
1651
1.0%
1641
1.0%
1631
1.0%
1622
2.0%
1592
2.0%
1582
2.0%

AGE
Real number (ℝ≥0)

HIGH CORRELATION

Distinct33
Distinct (%)32.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.0990099
Minimum44
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size936.0 B
2022-09-19T22:25:22.160749image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile55
Q165
median71
Q375
95-th percentile81
Maximum88
Range44
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.898740343
Coefficient of variation (CV)0.1126797704
Kurtosis0.822436965
Mean70.0990099
Median Absolute Deviation (MAD)4
Skewness-0.7177238122
Sum7080
Variance62.39009901
MonotonicityNot monotonic
2022-09-19T22:25:22.598523image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
758
 
7.9%
738
 
7.9%
727
 
6.9%
786
 
5.9%
676
 
5.9%
685
 
5.0%
655
 
5.0%
715
 
5.0%
764
 
4.0%
644
 
4.0%
Other values (23)43
42.6%
ValueCountFrequency (%)
441
1.0%
491
1.0%
521
1.0%
531
1.0%
541
1.0%
552
2.0%
561
1.0%
591
1.0%
602
2.0%
611
1.0%
ValueCountFrequency (%)
881
 
1.0%
832
 
2.0%
822
 
2.0%
812
 
2.0%
803
 
3.0%
791
 
1.0%
786
5.9%
772
 
2.0%
764
4.0%
758
7.9%

PackHistory
Real number (ℝ≥0)

HIGH CORRELATION

Distinct48
Distinct (%)47.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.6980198
Minimum1
Maximum109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size936.0 B
2022-09-19T22:25:23.141770image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q120
median36
Q354
95-th percentile90
Maximum109
Range108
Interquartile range (IQR)34

Descriptive statistics

Standard deviation24.55871324
Coefficient of variation (CV)0.6186382435
Kurtosis0.3628872921
Mean39.6980198
Median Absolute Deviation (MAD)16
Skewness0.7551818217
Sum4009.5
Variance603.130396
MonotonicityNot monotonic
2022-09-19T22:25:23.645440image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
3010
 
9.9%
208
 
7.9%
405
 
5.0%
505
 
5.0%
554
 
4.0%
754
 
4.0%
454
 
4.0%
603
 
3.0%
363
 
3.0%
33
 
3.0%
Other values (38)52
51.5%
ValueCountFrequency (%)
11
 
1.0%
33
3.0%
51
 
1.0%
63
3.0%
81
 
1.0%
91
 
1.0%
102
2.0%
112
2.0%
141
 
1.0%
153
3.0%
ValueCountFrequency (%)
1091
 
1.0%
1051
 
1.0%
1031
 
1.0%
1001
 
1.0%
902
2.0%
801
 
1.0%
781
 
1.0%
754
4.0%
681
 
1.0%
671
 
1.0%

COPDSEVERITY
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size936.0 B
MODERATE
43 
SEVERE
27 
MILD
23 
VERY SEVERE

Length

Max length11
Median length8
Mean length6.792079208
Min length4

Characters and Unicode

Total characters686
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSEVERE
2nd rowMODERATE
3rd rowMODERATE
4th rowVERY SEVERE
5th rowSEVERE

Common Values

ValueCountFrequency (%)
MODERATE43
42.6%
SEVERE27
26.7%
MILD23
22.8%
VERY SEVERE8
 
7.9%

Length

2022-09-19T22:25:23.921546image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-19T22:25:24.357599image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
moderate43
39.4%
severe35
32.1%
mild23
21.1%
very8
 
7.3%

Most occurring characters

ValueCountFrequency (%)
E199
29.0%
R86
12.5%
M66
 
9.6%
D66
 
9.6%
O43
 
6.3%
A43
 
6.3%
T43
 
6.3%
V43
 
6.3%
S35
 
5.1%
I23
 
3.4%
Other values (3)39
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter678
98.8%
Space Separator8
 
1.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E199
29.4%
R86
12.7%
M66
 
9.7%
D66
 
9.7%
O43
 
6.3%
A43
 
6.3%
T43
 
6.3%
V43
 
6.3%
S35
 
5.2%
I23
 
3.4%
Other values (2)31
 
4.6%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin678
98.8%
Common8
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
E199
29.4%
R86
12.7%
M66
 
9.7%
D66
 
9.7%
O43
 
6.3%
A43
 
6.3%
T43
 
6.3%
V43
 
6.3%
S35
 
5.2%
I23
 
3.4%
Other values (2)31
 
4.6%
Common
ValueCountFrequency (%)
8
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII686
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E199
29.0%
R86
12.5%
M66
 
9.6%
D66
 
9.6%
O43
 
6.3%
A43
 
6.3%
T43
 
6.3%
V43
 
6.3%
S35
 
5.1%
I23
 
3.4%
Other values (3)39
 
5.7%

MWT1
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct69
Distinct (%)69.7%
Missing2
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean385.8585859
Minimum120
Maximum688
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size936.0 B
2022-09-19T22:25:24.808327image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile212.7
Q1300
median419
Q3460.5
95-th percentile510.1
Maximum688
Range568
Interquartile range (IQR)160.5

Descriptive statistics

Standard deviation104.7441986
Coefficient of variation (CV)0.2714574781
Kurtosis-0.2030961038
Mean385.8585859
Median Absolute Deviation (MAD)59
Skewness-0.3110962365
Sum38200
Variance10971.34714
MonotonicityNot monotonic
2022-09-19T22:25:25.275442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4355
 
5.0%
4203
 
3.0%
3903
 
3.0%
4683
 
3.0%
3003
 
3.0%
4272
 
2.0%
2812
 
2.0%
4592
 
2.0%
4532
 
2.0%
4692
 
2.0%
Other values (59)72
71.3%
ValueCountFrequency (%)
1201
1.0%
1651
1.0%
2011
1.0%
2041
1.0%
2101
1.0%
2131
1.0%
2142
2.0%
2161
1.0%
2262
2.0%
2311
1.0%
ValueCountFrequency (%)
6881
1.0%
5761
1.0%
5581
1.0%
5221
1.0%
5111
1.0%
5102
2.0%
5011
1.0%
4962
2.0%
4952
2.0%
4922
2.0%

MWT2
Real number (ℝ≥0)

HIGH CORRELATION

Distinct72
Distinct (%)72.0%
Missing1
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean390.28
Minimum120
Maximum699
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size936.0 B
2022-09-19T22:25:25.793038image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile210
Q1303.75
median399
Q3459
95-th percentile541.15
Maximum699
Range579
Interquartile range (IQR)155.25

Descriptive statistics

Standard deviation107.7544984
Coefficient of variation (CV)0.2760953633
Kurtosis-0.1379340696
Mean390.28
Median Absolute Deviation (MAD)64.5
Skewness-0.1695204539
Sum39028
Variance11611.03192
MonotonicityNot monotonic
2022-09-19T22:25:26.207178image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4594
 
4.0%
3693
 
3.0%
2373
 
3.0%
4803
 
3.0%
5402
 
2.0%
4502
 
2.0%
4372
 
2.0%
4322
 
2.0%
4192
 
2.0%
3962
 
2.0%
Other values (62)75
74.3%
ValueCountFrequency (%)
1201
 
1.0%
1761
 
1.0%
1802
2.0%
2102
2.0%
2301
 
1.0%
2341
 
1.0%
2373
3.0%
2402
2.0%
2431
 
1.0%
2672
2.0%
ValueCountFrequency (%)
6991
1.0%
5821
1.0%
5771
1.0%
5751
1.0%
5631
1.0%
5402
2.0%
5312
2.0%
5251
1.0%
5181
1.0%
5051
1.0%

MWT1Best
Real number (ℝ≥0)

HIGH CORRELATION

Distinct71
Distinct (%)71.0%
Missing1
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean399.11
Minimum120
Maximum699
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size936.0 B
2022-09-19T22:25:26.492245image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile215.7
Q1303.75
median420
Q3465.25
95-th percentile540.9
Maximum699
Range579
Interquartile range (IQR)161.5

Descriptive statistics

Standard deviation106.5501158
Coefficient of variation (CV)0.2669692961
Kurtosis-0.1566638449
Mean399.11
Median Absolute Deviation (MAD)60
Skewness-0.2875998824
Sum39911
Variance11352.92717
MonotonicityIncreasing
2022-09-19T22:25:26.706228image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4595
 
5.0%
4803
 
3.0%
2373
 
3.0%
2403
 
3.0%
4513
 
3.0%
3003
 
3.0%
3902
 
2.0%
3752
 
2.0%
4892
 
2.0%
3692
 
2.0%
Other values (61)72
71.3%
ValueCountFrequency (%)
1201
 
1.0%
1761
 
1.0%
2011
 
1.0%
2102
2.0%
2161
 
1.0%
2373
3.0%
2403
3.0%
2461
 
1.0%
2701
 
1.0%
2712
2.0%
ValueCountFrequency (%)
6991
1.0%
5821
1.0%
5771
1.0%
5751
1.0%
5581
1.0%
5402
2.0%
5312
2.0%
5251
1.0%
5181
1.0%
5051
1.0%

FEV1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct85
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.603762376
Minimum0.45
Maximum3.18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size936.0 B
2022-09-19T22:25:26.902283image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.45
5-th percentile0.68
Q11.1
median1.6
Q31.96
95-th percentile2.9
Maximum3.18
Range2.73
Interquartile range (IQR)0.86

Descriptive statistics

Standard deviation0.672762739
Coefficient of variation (CV)0.4194902867
Kurtosis-0.4566982929
Mean1.603762376
Median Absolute Deviation (MAD)0.46
Skewness0.458892891
Sum161.98
Variance0.452609703
MonotonicityNot monotonic
2022-09-19T22:25:27.112912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.893
 
3.0%
1.922
 
2.0%
1.792
 
2.0%
1.112
 
2.0%
1.62
 
2.0%
2.432
 
2.0%
1.262
 
2.0%
0.722
 
2.0%
1.092
 
2.0%
1.462
 
2.0%
Other values (75)80
79.2%
ValueCountFrequency (%)
0.451
1.0%
0.471
1.0%
0.511
1.0%
0.61
1.0%
0.651
1.0%
0.682
2.0%
0.691
1.0%
0.722
2.0%
0.731
1.0%
0.741
1.0%
ValueCountFrequency (%)
3.181
1.0%
3.061
1.0%
3.021
1.0%
2.971
1.0%
2.931
1.0%
2.91
1.0%
2.81
1.0%
2.791
1.0%
2.751
1.0%
2.741
1.0%

FEV1PRED
Real number (ℝ≥0)

HIGH CORRELATION

Distinct51
Distinct (%)50.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.53148515
Minimum3.29
Maximum102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size936.0 B
2022-09-19T22:25:27.318868image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3.29
5-th percentile24
Q142
median60
Q375
95-th percentile93
Maximum102
Range98.71
Interquartile range (IQR)33

Descriptive statistics

Standard deviation22.29482139
Coefficient of variation (CV)0.380903053
Kurtosis-0.5362217926
Mean58.53148515
Median Absolute Deviation (MAD)16
Skewness-0.1658232952
Sum5911.68
Variance497.0590608
MonotonicityNot monotonic
2022-09-19T22:25:27.817311image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
375
 
5.0%
465
 
5.0%
854
 
4.0%
754
 
4.0%
624
 
4.0%
904
 
4.0%
614
 
4.0%
603
 
3.0%
443
 
3.0%
593
 
3.0%
Other values (41)62
61.4%
ValueCountFrequency (%)
3.291
1.0%
3.391
1.0%
141
1.0%
171
1.0%
242
2.0%
272
2.0%
291
1.0%
302
2.0%
322
2.0%
351
1.0%
ValueCountFrequency (%)
1021
 
1.0%
982
2.0%
951
 
1.0%
932
2.0%
921
 
1.0%
904
4.0%
881
 
1.0%
861
 
1.0%
854
4.0%
831
 
1.0%

FVC
Real number (ℝ≥0)

HIGH CORRELATION

Distinct80
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.954950495
Minimum1.14
Maximum5.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size936.0 B
2022-09-19T22:25:28.074637image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.14
5-th percentile1.56
Q12.27
median2.77
Q33.63
95-th percentile4.7
Maximum5.37
Range4.23
Interquartile range (IQR)1.36

Descriptive statistics

Standard deviation0.9762833848
Coefficient of variation (CV)0.3303890832
Kurtosis-0.4956385965
Mean2.954950495
Median Absolute Deviation (MAD)0.68
Skewness0.5064528152
Sum298.45
Variance0.9531292475
MonotonicityNot monotonic
2022-09-19T22:25:28.352396image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.295
 
5.0%
4.543
 
3.0%
2.063
 
3.0%
1.813
 
3.0%
2.092
 
2.0%
3.872
 
2.0%
2.332
 
2.0%
3.222
 
2.0%
3.632
 
2.0%
3.512
 
2.0%
Other values (70)75
74.3%
ValueCountFrequency (%)
1.141
 
1.0%
1.311
 
1.0%
1.472
2.0%
1.521
 
1.0%
1.561
 
1.0%
1.641
 
1.0%
1.813
3.0%
1.891
 
1.0%
1.991
 
1.0%
21
 
1.0%
ValueCountFrequency (%)
5.371
 
1.0%
5.231
 
1.0%
5.151
 
1.0%
4.91
 
1.0%
4.721
 
1.0%
4.71
 
1.0%
4.543
3.0%
4.461
 
1.0%
4.391
 
1.0%
4.381
 
1.0%

FVCPRED
Real number (ℝ≥0)

HIGH CORRELATION

Distinct57
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.43564356
Minimum27
Maximum132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size936.0 B
2022-09-19T22:25:28.581235image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile53
Q171
median84
Q3103
95-th percentile122
Maximum132
Range105
Interquartile range (IQR)32

Descriptive statistics

Standard deviation21.74001649
Coefficient of variation (CV)0.2515168001
Kurtosis-0.546740626
Mean86.43564356
Median Absolute Deviation (MAD)15
Skewness-0.0005357661001
Sum8730
Variance472.6283168
MonotonicityNot monotonic
2022-09-19T22:25:28.791043image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
845
 
5.0%
604
 
4.0%
724
 
4.0%
814
 
4.0%
704
 
4.0%
983
 
3.0%
753
 
3.0%
1253
 
3.0%
733
 
3.0%
793
 
3.0%
Other values (47)65
64.4%
ValueCountFrequency (%)
271
 
1.0%
451
 
1.0%
481
 
1.0%
511
 
1.0%
532
2.0%
552
2.0%
581
 
1.0%
604
4.0%
622
2.0%
631
 
1.0%
ValueCountFrequency (%)
1321
 
1.0%
1253
3.0%
1231
 
1.0%
1221
 
1.0%
1212
2.0%
1191
 
1.0%
1182
2.0%
1161
 
1.0%
1141
 
1.0%
1121
 
1.0%

CAT
Real number (ℝ≥0)

HIGH CORRELATION

Distinct30
Distinct (%)29.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.33663366
Minimum3
Maximum188
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size936.0 B
2022-09-19T22:25:29.047271image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q112
median18
Q324
95-th percentile30
Maximum188
Range185
Interquartile range (IQR)12

Descriptive statistics

Standard deviation18.67473011
Coefficient of variation (CV)0.9657694525
Kurtosis67.63924964
Mean19.33663366
Median Absolute Deviation (MAD)6
Skewness7.458913435
Sum1953
Variance348.7455446
MonotonicityNot monotonic
2022-09-19T22:25:29.221305image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
58
 
7.9%
238
 
7.9%
127
 
6.9%
256
 
5.9%
186
 
5.9%
226
 
5.9%
166
 
5.9%
145
 
5.0%
204
 
4.0%
93
 
3.0%
Other values (20)42
41.6%
ValueCountFrequency (%)
32
 
2.0%
41
 
1.0%
58
7.9%
62
 
2.0%
71
 
1.0%
93
 
3.0%
103
 
3.0%
113
 
3.0%
127
6.9%
132
 
2.0%
ValueCountFrequency (%)
1881
 
1.0%
322
 
2.0%
312
 
2.0%
303
3.0%
293
3.0%
282
 
2.0%
271
 
1.0%
263
3.0%
256
5.9%
243
3.0%

HAD
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct28
Distinct (%)27.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.18019802
Minimum0
Maximum56.2
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size936.0 B
2022-09-19T22:25:29.442869image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q315
95-th percentile26
Maximum56.2
Range56.2
Interquartile range (IQR)9

Descriptive statistics

Standard deviation8.5888069
Coefficient of variation (CV)0.7682159909
Kurtosis6.423029607
Mean11.18019802
Median Absolute Deviation (MAD)5
Skewness1.771283928
Sum1129.2
Variance73.76760396
MonotonicityNot monotonic
2022-09-19T22:25:29.638124image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
610
 
9.9%
77
 
6.9%
26
 
5.9%
126
 
5.9%
106
 
5.9%
96
 
5.9%
115
 
5.0%
15
 
5.0%
84
 
4.0%
184
 
4.0%
Other values (18)42
41.6%
ValueCountFrequency (%)
04
 
4.0%
15
5.0%
26
5.9%
31
 
1.0%
44
 
4.0%
53
 
3.0%
610
9.9%
77
6.9%
84
 
4.0%
96
5.9%
ValueCountFrequency (%)
56.21
 
1.0%
302
2.0%
291
 
1.0%
262
2.0%
232
2.0%
223
3.0%
212
2.0%
202
2.0%
192
2.0%
184
4.0%

SGRQ
Real number (ℝ≥0)

HIGH CORRELATION

Distinct89
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.18584158
Minimum2
Maximum77.44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size936.0 B
2022-09-19T22:25:29.919381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10.92
Q128.41
median38.21
Q355.23
95-th percentile72.24
Maximum77.44
Range75.44
Interquartile range (IQR)26.82

Descriptive statistics

Standard deviation18.23926771
Coefficient of variation (CV)0.4538729809
Kurtosis-0.6494983617
Mean40.18584158
Median Absolute Deviation (MAD)12.32
Skewness0.1892122922
Sum4058.77
Variance332.6708865
MonotonicityNot monotonic
2022-09-19T22:25:30.171717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.292
 
2.0%
55.562
 
2.0%
10.922
 
2.0%
61.972
 
2.0%
38.742
 
2.0%
28.412
 
2.0%
39.662
 
2.0%
32.382
 
2.0%
36.742
 
2.0%
15.052
 
2.0%
Other values (79)81
80.2%
ValueCountFrequency (%)
21
1.0%
8.121
1.0%
8.252
2.0%
10.011
1.0%
10.922
2.0%
15.052
2.0%
16.292
2.0%
17.951
1.0%
17.971
1.0%
18.721
1.0%
ValueCountFrequency (%)
77.441
1.0%
76.51
1.0%
75.561
1.0%
73.821
1.0%
72.561
1.0%
72.241
1.0%
71.211
1.0%
69.611
1.0%
69.551
1.0%
67.661
1.0%

AGEquartiles
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size936.0 B
3
28 
1
26 
2
24 
4
23 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row1
5th row1

Common Values

ValueCountFrequency (%)
328
27.7%
126
25.7%
224
23.8%
423
22.8%

Length

2022-09-19T22:25:30.391553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-19T22:25:30.576478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
328
27.7%
126
25.7%
224
23.8%
423
22.8%

Most occurring characters

ValueCountFrequency (%)
328
27.7%
126
25.7%
224
23.8%
423
22.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number101
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
328
27.7%
126
25.7%
224
23.8%
423
22.8%

Most occurring scripts

ValueCountFrequency (%)
Common101
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
328
27.7%
126
25.7%
224
23.8%
423
22.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
328
27.7%
126
25.7%
224
23.8%
423
22.8%

copd
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size936.0 B
2
43 
3
27 
1
23 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row4
5th row3

Common Values

ValueCountFrequency (%)
243
42.6%
327
26.7%
123
22.8%
48
 
7.9%

Length

2022-09-19T22:25:30.740087image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-19T22:25:30.923689image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
243
42.6%
327
26.7%
123
22.8%
48
 
7.9%

Most occurring characters

ValueCountFrequency (%)
243
42.6%
327
26.7%
123
22.8%
48
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number101
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
243
42.6%
327
26.7%
123
22.8%
48
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
Common101
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
243
42.6%
327
26.7%
123
22.8%
48
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
243
42.6%
327
26.7%
123
22.8%
48
 
7.9%

gender
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size936.0 B
1
65 
0
36 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
165
64.4%
036
35.6%

Length

2022-09-19T22:25:31.110488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-19T22:25:31.377720image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
165
64.4%
036
35.6%

Most occurring characters

ValueCountFrequency (%)
165
64.4%
036
35.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number101
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
165
64.4%
036
35.6%

Most occurring scripts

ValueCountFrequency (%)
Common101
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
165
64.4%
036
35.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
165
64.4%
036
35.6%

smoking
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size936.0 B
2
85 
1
16 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
285
84.2%
116
 
15.8%

Length

2022-09-19T22:25:31.641721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-19T22:25:31.977849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
285
84.2%
116
 
15.8%

Most occurring characters

ValueCountFrequency (%)
285
84.2%
116
 
15.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number101
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
285
84.2%
116
 
15.8%

Most occurring scripts

ValueCountFrequency (%)
Common101
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
285
84.2%
116
 
15.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
285
84.2%
116
 
15.8%

Diabetes
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size936.0 B
0
80 
1
21 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
080
79.2%
121
 
20.8%

Length

2022-09-19T22:25:32.717637image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-19T22:25:33.089971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
080
79.2%
121
 
20.8%

Most occurring characters

ValueCountFrequency (%)
080
79.2%
121
 
20.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number101
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
080
79.2%
121
 
20.8%

Most occurring scripts

ValueCountFrequency (%)
Common101
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
080
79.2%
121
 
20.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
080
79.2%
121
 
20.8%

muscular
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size936.0 B
0
82 
1
19 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
082
81.2%
119
 
18.8%

Length

2022-09-19T22:25:33.287332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-19T22:25:33.472525image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
082
81.2%
119
 
18.8%

Most occurring characters

ValueCountFrequency (%)
082
81.2%
119
 
18.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number101
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
082
81.2%
119
 
18.8%

Most occurring scripts

ValueCountFrequency (%)
Common101
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
082
81.2%
119
 
18.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
082
81.2%
119
 
18.8%

hypertension
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size936.0 B
0
89 
1
12 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
089
88.1%
112
 
11.9%

Length

2022-09-19T22:25:33.686650image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-19T22:25:33.875247image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
089
88.1%
112
 
11.9%

Most occurring characters

ValueCountFrequency (%)
089
88.1%
112
 
11.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number101
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
089
88.1%
112
 
11.9%

Most occurring scripts

ValueCountFrequency (%)
Common101
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
089
88.1%
112
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
089
88.1%
112
 
11.9%

AtrialFib
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size936.0 B
0
81 
1
20 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
081
80.2%
120
 
19.8%

Length

2022-09-19T22:25:34.022580image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-19T22:25:34.212404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
081
80.2%
120
 
19.8%

Most occurring characters

ValueCountFrequency (%)
081
80.2%
120
 
19.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number101
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
081
80.2%
120
 
19.8%

Most occurring scripts

ValueCountFrequency (%)
Common101
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
081
80.2%
120
 
19.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
081
80.2%
120
 
19.8%

IHD
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size936.0 B
0
92 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
092
91.1%
19
 
8.9%

Length

2022-09-19T22:25:34.322971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-19T22:25:34.488915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
092
91.1%
19
 
8.9%

Most occurring characters

ValueCountFrequency (%)
092
91.1%
19
 
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number101
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
092
91.1%
19
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
Common101
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
092
91.1%
19
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
092
91.1%
19
 
8.9%

Interactions

2022-09-19T22:25:16.511685image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:41.836330image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:45.690531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:48.935282image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:52.788059image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:55.516916image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:57.948986image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:00.216007image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:02.764926image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:05.176856image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:07.155385image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:09.509224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:11.738173image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:14.208860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:16.689072image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:42.099589image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:45.946996image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:49.191622image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:53.012096image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:55.666530image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:58.090483image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:00.606681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:02.926680image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:05.275239image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:07.290692image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:09.654028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:11.943290image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:14.402594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:16.864540image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:42.258029image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:46.211390image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:49.530070image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:53.262717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:55.845936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:58.333004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:00.770382image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:03.056993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:05.364122image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:07.457597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:09.803998image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:12.144553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:14.573829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:17.317133image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:42.468297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:46.443374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:49.877597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:53.425098image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:56.008564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:58.456636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:00.917304image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:03.229794image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:05.461617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:07.582703image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:09.956439image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:12.294276image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:14.730729image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:17.477809image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:42.686493image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:46.715620image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:50.172908image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:53.641388image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:56.370753image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:58.619971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:01.058595image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:03.391740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:05.591618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:07.730638image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:10.129885image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:12.428103image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:14.863885image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:17.655456image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:42.912307image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:46.960298image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:50.368292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:53.831483image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:56.523058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:58.759961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:01.233196image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:03.527502image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:05.771204image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:07.901923image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:10.293927image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:12.581679image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:15.040145image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:17.827089image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:43.210649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:47.209375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:50.671722image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:53.985064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:56.687603image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:58.910345image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:01.360586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:03.661611image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:05.922274image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:08.056597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:10.409348image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:12.946270image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:15.176588image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:17.991973image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:43.537971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:47.425086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:51.257598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:54.196311image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:56.849896image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:59.085563image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:01.576995image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:03.811310image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:06.095728image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:08.181219image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:10.578964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:13.160976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:15.368348image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:18.161810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:43.820772image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:47.691216image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:51.481902image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:54.399278image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:57.010716image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:59.199914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:01.764266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:03.981494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:06.211430image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:08.358838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:10.762318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:13.326503image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:15.520396image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:18.303715image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:44.097803image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:47.898885image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:51.631958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:54.564159image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:57.166185image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:59.407238image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:01.889194image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:04.117606image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:06.369442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:08.725358image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:10.922619image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:13.476267image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:15.666427image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:18.442549image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:44.663812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:48.114620image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:51.854703image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:54.734049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:57.350097image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:59.572075image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:02.085020image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:04.278624image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:06.510154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:08.887289image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:11.080886image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:13.607478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:15.817003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:18.605132image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:44.891780image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:48.247073image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:52.076288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:54.919504image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:57.505187image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:59.725419image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:02.250656image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:04.435097image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:06.671776image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:09.063919image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:11.198300image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:13.748598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:15.989814image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:18.754196image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:45.172905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:48.374235image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:52.308553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:55.121416image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:57.662056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:59.884325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:02.414997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:04.829081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:06.814337image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:09.226584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:11.349200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:13.921033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:16.153708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:18.842526image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:45.377871image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:48.610681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:52.511813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:55.318185image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:24:57.802355image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:00.050831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:02.579999image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:04.971704image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:06.967607image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:09.381362image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:11.520189image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:14.074506image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-19T22:25:16.335613image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-09-19T22:25:34.621753image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-19T22:25:35.028949image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-19T22:25:35.426807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-19T22:25:35.758159image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-19T22:25:36.049423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-19T22:25:19.125271image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-19T22:25:19.790640image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-09-19T22:25:20.114536image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-09-19T22:25:20.281262image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexIDAGEPackHistoryCOPDSEVERITYMWT1MWT2MWT1BestFEV1FEV1PREDFVCFVCPREDCATHADSGRQAGEquartilescopdgendersmokingDiabetesmuscularhypertensionAtrialFibIHD
01587760.0SEVERE120.0120.0120.01.2136.02.4098258.069.55431210010
12577950.0MODERATE165.0176.0176.01.0956.01.64651221.044.24420210011
23628011.0MODERATE201.0180.0201.01.5268.02.30862218.044.09420210010
341455660.0VERY SEVERE210.0210.0210.00.4714.01.14272826.062.04141200110
451366568.0SEVERE204.0210.0210.01.0742.02.91983218.075.56131201100
56846726.0MODERATE216.0180.0216.01.0950.01.99602921.073.82220110010
67936750.0SEVERE214.0237.0237.00.6935.01.31482930.077.44230110010
78278390.0SEVERE214.0237.0237.00.6832.02.2377222.045.41431210010
891147250.0MODERATE231.0237.0237.02.1363.04.3880256.069.61321110010
910152756.0SEVERE226.0240.0240.01.0646.02.06753120.055.56330201000

Last rows

df_indexIDAGEPackHistoryCOPDSEVERITYMWT1MWT2MWT1BestFEV1FEV1PREDFVCFVCPREDCATHADSGRQAGEquartilescopdgendersmokingDiabetesmuscularhypertensionAtrialFibIHD
91921496927.0MODERATE495.0531.0531.01.8969.02.617230.08.25221200000
92931636827.0MODERATE495.0531.0531.01.8969.02.617230.08.25221200000
93941125240.0MILD483.0540.0540.02.9375.03.6375122.025.62111100000
94951355540.0MILD522.0540.0540.02.7585.04.54962210.058.41111210001
9596127230.0MODERATE558.0563.0558.01.6161.03.14911812.034.64321200001
9697106825.0MILD511.0575.0575.02.7098.03.87108207.035.84211201000
9798437540.0MILD576.0577.0577.02.9093.04.72114922.015.05311201001
9899746830.0MODERATE468.0582.0582.01.6567.02.808841.019.70220200100
991001085430.0SEVERE688.0699.0699.01.7244.04.07821010.020.55131200000
100101857855.0MODERATENaNNaNNaN1.1554.02.0185187.030.21421200000